Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations22080
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.9 MiB
Average record size in memory752.9 B

Variable types

Numeric7
Text5
Categorical8

Alerts

age is highly overall correlated with overall and 1 other fieldsHigh correlation
club_overall is highly overall correlated with overall and 1 other fieldsHigh correlation
height_cm is highly overall correlated with weight_lbsHigh correlation
overall is highly overall correlated with age and 3 other fieldsHigh correlation
player_id is highly overall correlated with age and 1 other fieldsHigh correlation
position is highly overall correlated with pref_foot and 1 other fieldsHigh correlation
potential is highly overall correlated with club_overall and 1 other fieldsHigh correlation
pref_foot is highly overall correlated with positionHigh correlation
skill_moves is highly overall correlated with positionHigh correlation
weight_lbs is highly overall correlated with height_cmHigh correlation
international_reputation is highly imbalanced (83.8%) Imbalance
player_id has unique values Unique

Reproduction

Analysis started2025-08-14 00:22:14.344054
Analysis finished2025-08-14 00:22:17.100268
Duration2.76 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

player_id
Real number (ℝ)

High correlation  Unique 

Distinct22080
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238806.67
Minimum1179
Maximum275460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size172.6 KiB
2025-08-13T20:22:17.139270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1179
5-th percentile186597.85
Q1222433.25
median243590
Q3262079.5
95-th percentile272842.1
Maximum275460
Range274281
Interquartile range (IQR)39646.25

Descriptive statistics

Standard deviation28565.189
Coefficient of variation (CV)0.11961638
Kurtosis2.4450403
Mean238806.67
Median Absolute Deviation (MAD)19415.5
Skewness-1.1282007
Sum5.2728512 × 109
Variance8.1597 × 108
MonotonicityNot monotonic
2025-08-13T20:22:17.177778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
268103 1
 
< 0.1%
264783 1
 
< 0.1%
269395 1
 
< 0.1%
266090 1
 
< 0.1%
271466 1
 
< 0.1%
264300 1
 
< 0.1%
261759 1
 
< 0.1%
271752 1
 
< 0.1%
264146 1
 
< 0.1%
271078 1
 
< 0.1%
Other values (22070) 22070
> 99.9%
ValueCountFrequency (%)
1179 1
< 0.1%
2147 1
< 0.1%
3467 1
< 0.1%
18115 1
< 0.1%
18122 1
< 0.1%
19541 1
< 0.1%
20801 1
< 0.1%
24630 1
< 0.1%
25798 1
< 0.1%
41236 1
< 0.1%
ValueCountFrequency (%)
275460 1
< 0.1%
275448 1
< 0.1%
275447 1
< 0.1%
275445 1
< 0.1%
275444 1
< 0.1%
275442 1
< 0.1%
275441 1
< 0.1%
275440 1
< 0.1%
275439 1
< 0.1%
275438 1
< 0.1%
Distinct22045
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-08-13T20:22:17.290505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length39
Mean length18.074185
Min length2

Characters and Unicode

Total characters399078
Distinct characters1067
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22011 ?
Unique (%)99.7%

Sample

1st rowKylian Mbappé Lottin
2nd rowKevin De Bruyne
3rd rowRobert Lewandowski
4th rowKarim Benzema
5th rowLionel Andrés Messi Cuccittini
ValueCountFrequency (%)
daniel 328
 
0.6%
david 315
 
0.5%
josé 304
 
0.5%
de 284
 
0.5%
al 221
 
0.4%
silva 219
 
0.4%
juan 194
 
0.3%
james 186
 
0.3%
carlos 185
 
0.3%
alexander 176
 
0.3%
Other values (23638) 56501
95.9%
2025-08-13T20:22:17.442025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 39591
 
9.9%
36833
 
9.2%
e 30768
 
7.7%
o 25853
 
6.5%
i 25647
 
6.4%
n 25268
 
6.3%
r 24990
 
6.3%
l 18220
 
4.6%
s 15628
 
3.9%
u 10943
 
2.7%
Other values (1057) 145337
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 399078
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 39591
 
9.9%
36833
 
9.2%
e 30768
 
7.7%
o 25853
 
6.5%
i 25647
 
6.4%
n 25268
 
6.3%
r 24990
 
6.3%
l 18220
 
4.6%
s 15628
 
3.9%
u 10943
 
2.7%
Other values (1057) 145337
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 399078
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 39591
 
9.9%
36833
 
9.2%
e 30768
 
7.7%
o 25853
 
6.5%
i 25647
 
6.4%
n 25268
 
6.3%
r 24990
 
6.3%
l 18220
 
4.6%
s 15628
 
3.9%
u 10943
 
2.7%
Other values (1057) 145337
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 399078
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 39591
 
9.9%
36833
 
9.2%
e 30768
 
7.7%
o 25853
 
6.5%
i 25647
 
6.4%
n 25268
 
6.3%
r 24990
 
6.3%
l 18220
 
4.6%
s 15628
 
3.9%
u 10943
 
2.7%
Other values (1057) 145337
36.4%

age
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.894112
Minimum16
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size172.6 KiB
2025-08-13T20:22:17.476535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q121
median24
Q328
95-th percentile34
Maximum44
Range28
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8911168
Coefficient of variation (CV)0.19647685
Kurtosis-0.49524476
Mean24.894112
Median Absolute Deviation (MAD)4
Skewness0.43312981
Sum549662
Variance23.923023
MonotonicityNot monotonic
2025-08-13T20:22:17.509533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
22 1900
 
8.6%
21 1694
 
7.7%
20 1574
 
7.1%
25 1494
 
6.8%
23 1492
 
6.8%
26 1480
 
6.7%
24 1474
 
6.7%
19 1404
 
6.4%
27 1218
 
5.5%
30 1201
 
5.4%
Other values (18) 7149
32.4%
ValueCountFrequency (%)
16 165
 
0.7%
17 511
 
2.3%
18 1039
4.7%
19 1404
6.4%
20 1574
7.1%
21 1694
7.7%
22 1900
8.6%
23 1492
6.8%
24 1474
6.7%
25 1494
6.8%
ValueCountFrequency (%)
44 1
 
< 0.1%
42 2
 
< 0.1%
41 12
 
0.1%
40 18
 
0.1%
39 37
 
0.2%
38 83
 
0.4%
37 106
 
0.5%
36 171
 
0.8%
35 266
1.2%
34 440
2.0%
Distinct164
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-08-13T20:22:17.598567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length22
Mean length7.7622283
Min length4

Characters and Unicode

Total characters171390
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st rowFrance
2nd rowBelgium
3rd rowPoland
4th rowFrance
5th rowArgentina
ValueCountFrequency (%)
england 1812
 
7.0%
germany 1299
 
5.0%
argentina 1189
 
4.6%
spain 1149
 
4.5%
france 1033
 
4.0%
republic 965
 
3.7%
brazil 840
 
3.3%
italy 673
 
2.6%
ireland 535
 
2.1%
united 531
 
2.1%
Other values (182) 15709
61.0%
2025-08-13T20:22:17.729088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 23285
 
13.6%
n 16464
 
9.6%
e 13950
 
8.1%
r 11990
 
7.0%
i 11484
 
6.7%
l 9839
 
5.7%
o 6634
 
3.9%
d 6472
 
3.8%
t 6270
 
3.7%
u 5863
 
3.4%
Other values (45) 59139
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 23285
 
13.6%
n 16464
 
9.6%
e 13950
 
8.1%
r 11990
 
7.0%
i 11484
 
6.7%
l 9839
 
5.7%
o 6634
 
3.9%
d 6472
 
3.8%
t 6270
 
3.7%
u 5863
 
3.4%
Other values (45) 59139
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 23285
 
13.6%
n 16464
 
9.6%
e 13950
 
8.1%
r 11990
 
7.0%
i 11484
 
6.7%
l 9839
 
5.7%
o 6634
 
3.9%
d 6472
 
3.8%
t 6270
 
3.7%
u 5863
 
3.4%
Other values (45) 59139
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 23285
 
13.6%
n 16464
 
9.6%
e 13950
 
8.1%
r 11990
 
7.0%
i 11484
 
6.7%
l 9839
 
5.7%
o 6634
 
3.9%
d 6472
 
3.8%
t 6270
 
3.7%
u 5863
 
3.4%
Other values (45) 59139
34.5%

height_cm
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.39189
Minimum155
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size172.6 KiB
2025-08-13T20:22:17.766091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum155
5-th percentile170
Q1176
median181
Q3186
95-th percentile193
Maximum206
Range51
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8522744
Coefficient of variation (CV)0.037776078
Kurtosis-0.31104383
Mean181.39189
Median Absolute Deviation (MAD)5
Skewness-0.021494583
Sum4005133
Variance46.953664
MonotonicityNot monotonic
2025-08-13T20:22:17.804603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
180 1721
 
7.8%
185 1544
 
7.0%
178 1470
 
6.7%
175 1295
 
5.9%
183 1272
 
5.8%
188 1122
 
5.1%
182 1034
 
4.7%
186 906
 
4.1%
184 898
 
4.1%
177 851
 
3.9%
Other values (39) 9967
45.1%
ValueCountFrequency (%)
155 1
 
< 0.1%
156 3
 
< 0.1%
158 3
 
< 0.1%
160 6
 
< 0.1%
161 8
 
< 0.1%
162 15
 
0.1%
163 34
0.2%
164 34
0.2%
165 84
0.4%
166 67
0.3%
ValueCountFrequency (%)
206 2
 
< 0.1%
204 2
 
< 0.1%
203 7
 
< 0.1%
202 10
 
< 0.1%
201 14
 
0.1%
200 10
 
< 0.1%
199 20
 
0.1%
198 64
0.3%
197 71
0.3%
196 156
0.7%

weight_lbs
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.39946
Minimum108
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size172.6 KiB
2025-08-13T20:22:17.842603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile141
Q1154
median165
Q3176
95-th percentile192
Maximum231
Range123
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.411979
Coefficient of variation (CV)0.093180347
Kurtosis0.071041754
Mean165.39946
Median Absolute Deviation (MAD)11
Skewness0.19862655
Sum3652020
Variance237.52909
MonotonicityNot monotonic
2025-08-13T20:22:17.881121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 1775
 
8.0%
154 1694
 
7.7%
176 1329
 
6.0%
159 1252
 
5.7%
172 1247
 
5.6%
161 1169
 
5.3%
163 1102
 
5.0%
168 1057
 
4.8%
170 949
 
4.3%
150 864
 
3.9%
Other values (45) 9642
43.7%
ValueCountFrequency (%)
108 1
 
< 0.1%
115 1
 
< 0.1%
117 2
 
< 0.1%
119 5
 
< 0.1%
121 14
 
0.1%
123 18
 
0.1%
126 27
 
0.1%
128 50
 
0.2%
130 46
 
0.2%
132 204
0.9%
ValueCountFrequency (%)
231 1
 
< 0.1%
229 2
 
< 0.1%
227 1
 
< 0.1%
225 3
 
< 0.1%
223 2
 
< 0.1%
220 5
 
< 0.1%
218 6
 
< 0.1%
216 12
0.1%
214 13
0.1%
212 21
0.1%

position
Categorical

High correlation 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
CB
3876 
ST
3113 
CM
2590 
GK
2430 
CDM
1829 
Other values (10)
8242 

Length

Max length3
Median length2
Mean length2.1718297
Min length2

Characters and Unicode

Total characters47954
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowCM
3rd rowST
4th rowCF
5th rowRW

Common Values

ValueCountFrequency (%)
CB 3876
17.6%
ST 3113
14.1%
CM 2590
11.7%
GK 2430
11.0%
CDM 1829
8.3%
RB 1416
 
6.4%
LB 1379
 
6.2%
CAM 1267
 
5.7%
RM 1145
 
5.2%
LM 1143
 
5.2%
Other values (5) 1892
8.6%

Length

2025-08-13T20:22:17.918123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cb 3876
17.6%
st 3113
14.1%
cm 2590
11.7%
gk 2430
11.0%
cdm 1829
8.3%
rb 1416
 
6.4%
lb 1379
 
6.2%
cam 1267
 
5.7%
rm 1145
 
5.2%
lm 1143
 
5.2%
Other values (5) 1892
8.6%

Most occurring characters

ValueCountFrequency (%)
C 9702
20.2%
M 7974
16.6%
B 7369
15.4%
R 3454
 
7.2%
L 3381
 
7.1%
T 3113
 
6.5%
S 3113
 
6.5%
K 2430
 
5.1%
G 2430
 
5.1%
D 1829
 
3.8%
Other values (3) 3159
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47954
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 9702
20.2%
M 7974
16.6%
B 7369
15.4%
R 3454
 
7.2%
L 3381
 
7.1%
T 3113
 
6.5%
S 3113
 
6.5%
K 2430
 
5.1%
G 2430
 
5.1%
D 1829
 
3.8%
Other values (3) 3159
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47954
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 9702
20.2%
M 7974
16.6%
B 7369
15.4%
R 3454
 
7.2%
L 3381
 
7.1%
T 3113
 
6.5%
S 3113
 
6.5%
K 2430
 
5.1%
G 2430
 
5.1%
D 1829
 
3.8%
Other values (3) 3159
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47954
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 9702
20.2%
M 7974
16.6%
B 7369
15.4%
R 3454
 
7.2%
L 3381
 
7.1%
T 3113
 
6.5%
S 3113
 
6.5%
K 2430
 
5.1%
G 2430
 
5.1%
D 1829
 
3.8%
Other values (3) 3159
 
6.6%

overall
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.223958
Minimum46
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size172.6 KiB
2025-08-13T20:22:17.952178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile53
Q161
median65
Q370
95-th percentile76
Maximum91
Range45
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.804443
Coefficient of variation (CV)0.10432429
Kurtosis0.17612723
Mean65.223958
Median Absolute Deviation (MAD)4
Skewness0.061983962
Sum1440145
Variance46.300445
MonotonicityNot monotonic
2025-08-13T20:22:18.104208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
64 1494
 
6.8%
66 1489
 
6.7%
65 1482
 
6.7%
67 1387
 
6.3%
63 1358
 
6.2%
62 1208
 
5.5%
68 1166
 
5.3%
69 1069
 
4.8%
70 977
 
4.4%
61 914
 
4.1%
Other values (36) 9536
43.2%
ValueCountFrequency (%)
46 13
 
0.1%
47 35
 
0.2%
48 53
 
0.2%
49 86
 
0.4%
50 155
 
0.7%
51 185
0.8%
52 288
1.3%
53 329
1.5%
54 388
1.8%
55 416
1.9%
ValueCountFrequency (%)
91 5
 
< 0.1%
90 1
 
< 0.1%
89 11
 
< 0.1%
88 10
 
< 0.1%
87 9
 
< 0.1%
86 22
 
0.1%
85 32
 
0.1%
84 51
0.2%
83 55
0.2%
82 83
0.4%

potential
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.458107
Minimum46
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size172.6 KiB
2025-08-13T20:22:18.142208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile61
Q166
median70
Q374
95-th percentile81
Maximum95
Range49
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.1478133
Coefficient of variation (CV)0.087254875
Kurtosis0.097018406
Mean70.458107
Median Absolute Deviation (MAD)4
Skewness0.16884268
Sum1555715
Variance37.795609
MonotonicityNot monotonic
2025-08-13T20:22:18.181234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
70 1487
 
6.7%
68 1463
 
6.6%
69 1434
 
6.5%
72 1346
 
6.1%
71 1322
 
6.0%
67 1284
 
5.8%
74 1254
 
5.7%
73 1235
 
5.6%
66 1210
 
5.5%
65 1059
 
4.8%
Other values (38) 8986
40.7%
ValueCountFrequency (%)
46 1
 
< 0.1%
48 2
 
< 0.1%
49 2
 
< 0.1%
50 4
 
< 0.1%
51 11
 
< 0.1%
52 17
 
0.1%
53 17
 
0.1%
54 50
0.2%
55 51
0.2%
56 78
0.4%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 1
 
< 0.1%
92 5
 
< 0.1%
91 10
 
< 0.1%
90 13
 
0.1%
89 33
 
0.1%
88 32
 
0.1%
87 38
 
0.2%
86 93
0.4%
85 106
0.5%
Distinct248
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-13T20:22:18.287071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.4772192
Min length1

Characters and Unicode

Total characters76777
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.2%

Sample

1st row190,5
2nd row107,5
3rd row84
4th row64
5th row54
ValueCountFrequency (%)
1,1 758
 
3.4%
1,2 728
 
3.3%
1 646
 
2.9%
1,3 620
 
2.8%
1,6 545
 
2.5%
0,475 503
 
2.3%
1,4 483
 
2.2%
0,5 479
 
2.2%
1,5 464
 
2.1%
1,9 435
 
2.0%
Other values (238) 16419
74.4%
2025-08-13T20:22:18.426104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 19915
25.9%
0 12263
16.0%
5 10830
14.1%
1 8811
11.5%
2 7325
 
9.5%
7 4608
 
6.0%
3 3769
 
4.9%
4 3076
 
4.0%
6 2404
 
3.1%
9 1898
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76777
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 19915
25.9%
0 12263
16.0%
5 10830
14.1%
1 8811
11.5%
2 7325
 
9.5%
7 4608
 
6.0%
3 3769
 
4.9%
4 3076
 
4.0%
6 2404
 
3.1%
9 1898
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76777
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 19915
25.9%
0 12263
16.0%
5 10830
14.1%
1 8811
11.5%
2 7325
 
9.5%
7 4608
 
6.0%
3 3769
 
4.9%
4 3076
 
4.0%
6 2404
 
3.1%
9 1898
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76777
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 19915
25.9%
0 12263
16.0%
5 10830
14.1%
1 8811
11.5%
2 7325
 
9.5%
7 4608
 
6.0%
3 3769
 
4.9%
4 3076
 
4.0%
6 2404
 
3.1%
9 1898
 
2.5%

pref_foot
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Right
16733 
Left
5347 

Length

Max length5
Median length5
Mean length4.7578351
Min length4

Characters and Unicode

Total characters105053
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRight
2nd rowRight
3rd rowRight
4th rowRight
5th rowLeft

Common Values

ValueCountFrequency (%)
Right 16733
75.8%
Left 5347
 
24.2%

Length

2025-08-13T20:22:18.463110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T20:22:18.489283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
right 16733
75.8%
left 5347
 
24.2%

Most occurring characters

ValueCountFrequency (%)
t 22080
21.0%
R 16733
15.9%
g 16733
15.9%
i 16733
15.9%
h 16733
15.9%
L 5347
 
5.1%
e 5347
 
5.1%
f 5347
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 22080
21.0%
R 16733
15.9%
g 16733
15.9%
i 16733
15.9%
h 16733
15.9%
L 5347
 
5.1%
e 5347
 
5.1%
f 5347
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 22080
21.0%
R 16733
15.9%
g 16733
15.9%
i 16733
15.9%
h 16733
15.9%
L 5347
 
5.1%
e 5347
 
5.1%
f 5347
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 22080
21.0%
R 16733
15.9%
g 16733
15.9%
i 16733
15.9%
h 16733
15.9%
L 5347
 
5.1%
e 5347
 
5.1%
f 5347
 
5.1%

weak_foot
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
3
13651 
2
4826 
4
3042 
5
 
355
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22080
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 13651
61.8%
2 4826
 
21.9%
4 3042
 
13.8%
5 355
 
1.6%
1 206
 
0.9%

Length

2025-08-13T20:22:18.513283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T20:22:18.534282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 13651
61.8%
2 4826
 
21.9%
4 3042
 
13.8%
5 355
 
1.6%
1 206
 
0.9%

Most occurring characters

ValueCountFrequency (%)
3 13651
61.8%
2 4826
 
21.9%
4 3042
 
13.8%
5 355
 
1.6%
1 206
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 13651
61.8%
2 4826
 
21.9%
4 3042
 
13.8%
5 355
 
1.6%
1 206
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 13651
61.8%
2 4826
 
21.9%
4 3042
 
13.8%
5 355
 
1.6%
1 206
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 13651
61.8%
2 4826
 
21.9%
4 3042
 
13.8%
5 355
 
1.6%
1 206
 
0.9%

skill_moves
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2
10626 
3
7660 
1
2430 
4
1313 
5
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22080
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
2 10626
48.1%
3 7660
34.7%
1 2430
 
11.0%
4 1313
 
5.9%
5 51
 
0.2%

Length

2025-08-13T20:22:18.563287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T20:22:18.585794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 10626
48.1%
3 7660
34.7%
1 2430
 
11.0%
4 1313
 
5.9%
5 51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 10626
48.1%
3 7660
34.7%
1 2430
 
11.0%
4 1313
 
5.9%
5 51
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 10626
48.1%
3 7660
34.7%
1 2430
 
11.0%
4 1313
 
5.9%
5 51
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 10626
48.1%
3 7660
34.7%
1 2430
 
11.0%
4 1313
 
5.9%
5 51
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 10626
48.1%
3 7660
34.7%
1 2430
 
11.0%
4 1313
 
5.9%
5 51
 
0.2%

international_reputation
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
20809 
2
 
949
3
 
260
4
 
55
5
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22080
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row5
4th row4
5th row5

Common Values

ValueCountFrequency (%)
1 20809
94.2%
2 949
 
4.3%
3 260
 
1.2%
4 55
 
0.2%
5 7
 
< 0.1%

Length

2025-08-13T20:22:18.613795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T20:22:18.634794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 20809
94.2%
2 949
 
4.3%
3 260
 
1.2%
4 55
 
0.2%
5 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 20809
94.2%
2 949
 
4.3%
3 260
 
1.2%
4 55
 
0.2%
5 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20809
94.2%
2 949
 
4.3%
3 260
 
1.2%
4 55
 
0.2%
5 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20809
94.2%
2 949
 
4.3%
3 260
 
1.2%
4 55
 
0.2%
5 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20809
94.2%
2 949
 
4.3%
3 260
 
1.2%
4 55
 
0.2%
5 7
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Medium
14761 
High
5885 
Low
 
1434

Length

Max length6
Median length6
Mean length5.2721014
Min length3

Characters and Unicode

Total characters116408
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowMedium
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 14761
66.9%
High 5885
 
26.7%
Low 1434
 
6.5%

Length

2025-08-13T20:22:18.664798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T20:22:18.687815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 14761
66.9%
high 5885
 
26.7%
low 1434
 
6.5%

Most occurring characters

ValueCountFrequency (%)
i 20646
17.7%
M 14761
12.7%
e 14761
12.7%
d 14761
12.7%
u 14761
12.7%
m 14761
12.7%
H 5885
 
5.1%
g 5885
 
5.1%
h 5885
 
5.1%
L 1434
 
1.2%
Other values (2) 2868
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 20646
17.7%
M 14761
12.7%
e 14761
12.7%
d 14761
12.7%
u 14761
12.7%
m 14761
12.7%
H 5885
 
5.1%
g 5885
 
5.1%
h 5885
 
5.1%
L 1434
 
1.2%
Other values (2) 2868
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 20646
17.7%
M 14761
12.7%
e 14761
12.7%
d 14761
12.7%
u 14761
12.7%
m 14761
12.7%
H 5885
 
5.1%
g 5885
 
5.1%
h 5885
 
5.1%
L 1434
 
1.2%
Other values (2) 2868
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 20646
17.7%
M 14761
12.7%
e 14761
12.7%
d 14761
12.7%
u 14761
12.7%
m 14761
12.7%
H 5885
 
5.1%
g 5885
 
5.1%
h 5885
 
5.1%
L 1434
 
1.2%
Other values (2) 2868
 
2.5%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Medium
16169 
High
3720 
Low
2191 

Length

Max length7
Median length7
Mean length6.3653533
Min length4

Characters and Unicode

Total characters140547
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Low
2nd row Medium
3rd row Medium
4th row Medium
5th row Low

Common Values

ValueCountFrequency (%)
Medium 16169
73.2%
High 3720
 
16.8%
Low 2191
 
9.9%

Length

2025-08-13T20:22:18.715815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T20:22:18.737815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 16169
73.2%
high 3720
 
16.8%
low 2191
 
9.9%

Most occurring characters

ValueCountFrequency (%)
22080
15.7%
i 19889
14.2%
M 16169
11.5%
e 16169
11.5%
d 16169
11.5%
u 16169
11.5%
m 16169
11.5%
H 3720
 
2.6%
g 3720
 
2.6%
h 3720
 
2.6%
Other values (3) 6573
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22080
15.7%
i 19889
14.2%
M 16169
11.5%
e 16169
11.5%
d 16169
11.5%
u 16169
11.5%
m 16169
11.5%
H 3720
 
2.6%
g 3720
 
2.6%
h 3720
 
2.6%
Other values (3) 6573
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22080
15.7%
i 19889
14.2%
M 16169
11.5%
e 16169
11.5%
d 16169
11.5%
u 16169
11.5%
m 16169
11.5%
H 3720
 
2.6%
g 3720
 
2.6%
h 3720
 
2.6%
Other values (3) 6573
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22080
15.7%
i 19889
14.2%
M 16169
11.5%
e 16169
11.5%
d 16169
11.5%
u 16169
11.5%
m 16169
11.5%
H 3720
 
2.6%
g 3720
 
2.6%
h 3720
 
2.6%
Other values (3) 6573
 
4.7%

body_type
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Normal (170-185)
7629 
Normal (185+)
4873 
Lean (170-185)
4636 
Lean (185+)
2351 
Normal (170-)
785 
Other values (5)
1806 

Length

Max length16
Median length14
Mean length14.034601
Min length6

Characters and Unicode

Total characters309884
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnique
2nd rowUnique
3rd rowUnique
4th rowNormal (170-185)
5th rowUnique

Common Values

ValueCountFrequency (%)
Normal (170-185) 7629
34.6%
Normal (185+) 4873
22.1%
Lean (170-185) 4636
21.0%
Lean (185+) 2351
 
10.6%
Normal (170-) 785
 
3.6%
Stocky (170-185) 639
 
2.9%
Lean (170-) 492
 
2.2%
Stocky (185+) 437
 
2.0%
Unique 130
 
0.6%
Stocky (170-) 108
 
0.5%

Length

2025-08-13T20:22:18.766819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T20:22:18.797327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 13287
30.2%
170-185 12904
29.3%
185 7661
17.4%
lean 7479
17.0%
170 1385
 
3.1%
stocky 1184
 
2.7%
unique 130
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 34854
 
11.2%
( 21950
 
7.1%
21950
 
7.1%
) 21950
 
7.1%
a 20766
 
6.7%
8 20565
 
6.6%
5 20565
 
6.6%
o 14471
 
4.7%
- 14289
 
4.6%
0 14289
 
4.6%
Other values (18) 104235
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 309884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 34854
 
11.2%
( 21950
 
7.1%
21950
 
7.1%
) 21950
 
7.1%
a 20766
 
6.7%
8 20565
 
6.6%
5 20565
 
6.6%
o 14471
 
4.7%
- 14289
 
4.6%
0 14289
 
4.6%
Other values (18) 104235
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 309884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 34854
 
11.2%
( 21950
 
7.1%
21950
 
7.1%
) 21950
 
7.1%
a 20766
 
6.7%
8 20565
 
6.6%
5 20565
 
6.6%
o 14471
 
4.7%
- 14289
 
4.6%
0 14289
 
4.6%
Other values (18) 104235
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 309884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 34854
 
11.2%
( 21950
 
7.1%
21950
 
7.1%
) 21950
 
7.1%
a 20766
 
6.7%
8 20565
 
6.6%
5 20565
 
6.6%
o 14471
 
4.7%
- 14289
 
4.6%
0 14289
 
4.6%
Other values (18) 104235
33.6%
Distinct54
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-08-13T20:22:18.859331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length19
Mean length7.599683
Min length4

Characters and Unicode

Total characters167801
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowEngland
3rd rowSpain
4th rowSpain
5th rowFrance
ValueCountFrequency (%)
england 2741
 
10.8%
germany 1700
 
6.7%
spain 1355
 
5.3%
italy 1178
 
4.6%
france 1167
 
4.6%
argentina 949
 
3.7%
united 860
 
3.4%
states 832
 
3.3%
republic 814
 
3.2%
pr 600
 
2.4%
Other values (53) 13176
51.9%
2025-08-13T20:22:18.949835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 22458
 
13.4%
n 17625
 
10.5%
e 13308
 
7.9%
r 10826
 
6.5%
l 10197
 
6.1%
i 10002
 
6.0%
d 7591
 
4.5%
t 7372
 
4.4%
u 5799
 
3.5%
g 5466
 
3.3%
Other values (36) 57157
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 167801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 22458
 
13.4%
n 17625
 
10.5%
e 13308
 
7.9%
r 10826
 
6.5%
l 10197
 
6.1%
i 10002
 
6.0%
d 7591
 
4.5%
t 7372
 
4.4%
u 5799
 
3.5%
g 5466
 
3.3%
Other values (36) 57157
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 167801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 22458
 
13.4%
n 17625
 
10.5%
e 13308
 
7.9%
r 10826
 
6.5%
l 10197
 
6.1%
i 10002
 
6.0%
d 7591
 
4.5%
t 7372
 
4.4%
u 5799
 
3.5%
g 5466
 
3.3%
Other values (36) 57157
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 167801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 22458
 
13.4%
n 17625
 
10.5%
e 13308
 
7.9%
r 10826
 
6.5%
l 10197
 
6.1%
i 10002
 
6.0%
d 7591
 
4.5%
t 7372
 
4.4%
u 5799
 
3.5%
g 5466
 
3.3%
Other values (36) 57157
34.1%
Distinct732
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-08-13T20:22:19.024346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length18
Mean length10.948822
Min length2

Characters and Unicode

Total characters241750
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowParis Saint Germain
2nd rowManchester City
3rd rowFc Barcelona
4th rowReal Madrid
5th rowParis Saint Germain
ValueCountFrequency (%)
united 765
 
2.1%
city 621
 
1.7%
fc 470
 
1.3%
al 467
 
1.3%
town 364
 
1.0%
de 281
 
0.8%
real 239
 
0.7%
rovers 222
 
0.6%
deportivo 221
 
0.6%
sporting 216
 
0.6%
Other values (926) 31865
89.2%
2025-08-13T20:22:19.134858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 21737
 
9.0%
e 21237
 
8.8%
n 16804
 
7.0%
r 15909
 
6.6%
i 14839
 
6.1%
o 14761
 
6.1%
13651
 
5.6%
t 12015
 
5.0%
l 11689
 
4.8%
s 9441
 
3.9%
Other values (86) 89667
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 21737
 
9.0%
e 21237
 
8.8%
n 16804
 
7.0%
r 15909
 
6.6%
i 14839
 
6.1%
o 14761
 
6.1%
13651
 
5.6%
t 12015
 
5.0%
l 11689
 
4.8%
s 9441
 
3.9%
Other values (86) 89667
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 21737
 
9.0%
e 21237
 
8.8%
n 16804
 
7.0%
r 15909
 
6.6%
i 14839
 
6.1%
o 14761
 
6.1%
13651
 
5.6%
t 12015
 
5.0%
l 11689
 
4.8%
s 9441
 
3.9%
Other values (86) 89667
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 21737
 
9.0%
e 21237
 
8.8%
n 16804
 
7.0%
r 15909
 
6.6%
i 14839
 
6.1%
o 14761
 
6.1%
13651
 
5.6%
t 12015
 
5.0%
l 11689
 
4.8%
s 9441
 
3.9%
Other values (86) 89667
37.1%

club_overall
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.295743
Minimum0
Maximum85
Zeros19
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size172.6 KiB
2025-08-13T20:22:19.168860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q165
median68
Q371
95-th percentile78
Maximum85
Range85
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.3585547
Coefficient of variation (CV)0.078461035
Kurtosis22.516994
Mean68.295743
Median Absolute Deviation (MAD)3
Skewness-1.233215
Sum1507970
Variance28.714108
MonotonicityNot monotonic
2025-08-13T20:22:19.203367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
69 2674
12.1%
67 2050
 
9.3%
65 1921
 
8.7%
66 1752
 
7.9%
68 1744
 
7.9%
70 1677
 
7.6%
64 1610
 
7.3%
63 1123
 
5.1%
71 923
 
4.2%
61 684
 
3.1%
Other values (24) 5922
26.8%
ValueCountFrequency (%)
0 19
 
0.1%
52 4
 
< 0.1%
54 31
 
0.1%
55 29
 
0.1%
56 44
 
0.2%
57 62
 
0.3%
58 131
 
0.6%
59 68
 
0.3%
60 170
 
0.8%
61 684
3.1%
ValueCountFrequency (%)
85 96
 
0.4%
84 65
 
0.3%
83 139
 
0.6%
82 93
 
0.4%
81 112
 
0.5%
80 121
 
0.5%
79 304
1.4%
78 249
1.1%
77 360
1.6%
76 550
2.5%

Interactions

2025-08-13T20:22:16.603703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.136840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.376075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.718105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.940129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.169154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.385683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.635703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.170843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.410075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.751107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.973640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.201660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.418683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.667707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.206348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.444074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.785614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.006639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.233660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.450686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.698215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.239350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.478582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.814612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.039638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.262665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.483192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.730215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.272861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.616090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.846613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.070644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.295170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.514191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.760220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.305859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.650089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.878128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.104152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.324170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.544191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.790728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.339860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.684596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:15.909129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.135151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.356170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T20:22:16.574704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-13T20:22:19.235368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Attacking_work_rateDefensive_work_rateagebody_typeclub_overallheight_cminternational_reputationoverallplayer_idpositionpotentialpref_footskill_movesweak_footweight_lbs
Attacking_work_rate1.0000.1620.0970.1660.0570.1760.0360.1350.0920.3500.1030.0650.2840.1270.143
Defensive_work_rate0.1621.0000.1090.0860.0370.0760.0470.1180.0980.3400.0460.0290.2130.0730.062
age0.0970.1091.0000.0730.0240.0650.1390.501-0.7860.057-0.2820.0250.0990.0550.217
body_type0.1660.0860.0731.0000.1240.4410.2830.1820.0700.2090.1130.0780.2440.0830.285
club_overall0.0570.0370.0240.1241.0000.0550.2130.590-0.1160.0400.6560.0200.1220.0590.063
height_cm0.1760.0760.0650.4410.0551.0000.0200.028-0.0700.2190.0210.0810.2390.0840.758
international_reputation0.0360.0470.1390.2830.2130.0201.0000.4220.1980.0420.2330.0000.1670.0750.076
overall0.1350.1180.5010.1820.5900.0280.4221.000-0.5450.0500.5850.0520.2270.1120.129
player_id0.0920.098-0.7860.070-0.116-0.0700.198-0.5451.0000.0400.0820.0280.1240.071-0.171
position0.3500.3400.0570.2090.0400.2190.0420.0500.0401.0000.0410.5010.5830.1870.196
potential0.1030.046-0.2820.1130.6560.0210.2330.5850.0820.0411.0000.0400.1680.080-0.012
pref_foot0.0650.0290.0250.0780.0200.0810.0000.0520.0280.5010.0401.0000.1090.0920.082
skill_moves0.2840.2130.0990.2440.1220.2390.1670.2270.1240.5830.1680.1091.0000.2000.197
weak_foot0.1270.0730.0550.0830.0590.0840.0750.1120.0710.1870.0800.0920.2001.0000.068
weight_lbs0.1430.0620.2170.2850.0630.7580.0760.129-0.1710.196-0.0120.0820.1970.0681.000

Missing values

2025-08-13T20:22:16.956002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-13T20:22:17.023510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

player_idplayer_nameagecountryheight_cmweight_lbspositionoverallpotentialValue_millionspref_footweak_footskill_movesinternational_reputationAttacking_work_rateDefensive_work_ratebody_typeclub_countryclub_nameclub_overall
0231747Kylian Mbappé Lottin23France182161ST9195190,5Right454HighLowUniqueFranceParis Saint Germain84
1192985Kevin De Bruyne31Belgium181165CM9191107,5Right544HighMediumUniqueEnglandManchester City85
2188545Robert Lewandowski33Poland185179ST919184Right445HighMediumUniqueSpainFc Barcelona83
3165153Karim Benzema34France185179CF919164Right444MediumMediumNormal (170-185)SpainReal Madrid85
4158023Lionel Andrés Messi Cuccittini35Argentina169148RW919154Left445LowLowUniqueFranceParis Saint Germain84
5239085Erling Braut Haaland21Norway195207ST8994160Left334HighMediumUniqueEnglandManchester City85
6192119Thibaut Nicolas Marc Courtois30Belgium199212GK909190Left314MediumMediumUniqueSpainReal Madrid85
7212831Alisson Ramsés Becker29Brazil193201GK899079Right313MediumMediumUniqueEnglandLiverpool84
8212622Joshua Walter Kimmich27Germany177165CDM8990105,5Right434HighMediumNormal (170-185)GermanyFc Bayern München85
9209331Mohamed Salah Ghaly30Egypt175157RW898999,5Left344HighMediumUniqueEnglandLiverpool84
player_idplayer_nameagecountryheight_cmweight_lbspositionoverallpotentialValue_millionspref_footweak_footskill_movesinternational_reputationAttacking_work_rateDefensive_work_ratebody_typeclub_countryclub_nameclub_overall
22070267824Daithí McCallion17Republic of Ireland178143CB47610,1Right321MediumMediumLean (170-185)Northern IrelandDerry City61
22071261424Nabin Rabha25India176146LB47500,06Left321MediumMediumLean (170-185)IndiaNortheast United57
22072265566Subhadip Majhi23India175154CM47550,1Right321MediumMediumNormal (170-185)IndiaChennaiyin59
22073255856Sandip Mandi20India174161LB47580,1Left321MediumMediumNormal (170-185)IndiaJamshedpur58
22074271587Turki Mohammed Al-Dhafiri19Saudi Arabia174148LW47590,11Left321MediumMediumNormal (170-185)Saudi ArabiaAl Batin63
22075261876Conan Noonan19Republic of Ireland180148ST52630,17Right321MediumMediumLean (170-185)Republic of IrelandShamrock Rovers63
22076254232Mohammed Asif Khan21India171121CAM46580,12Right221LowMediumLean (170-185)IndiaMumbai City62
22077259213Antonio D'Silva22India182161GK51610,11Right311MediumMediumNormal (170-185)IndiaOdisha Fc58
22078258802Bhupender Singh22India172157RM46540,11Right221MediumMediumLean (170-185)IndiaJamshedpur58
22079271608Aqeel Al Dhafeeri17Saudi Arabia180154CB46630,11Right321MediumMediumLean (170-185)Saudi ArabiaAl Batin59